Determining hazard of an aneurysm by change determination

ABSTRACT

Systems, methods and apparatus are provided through which in some implementations changes in an aneurysm in a patient over time are identified by determining temporal differences between segmented aneurysms in a plurality of longitudinal exams and visually presenting the temporal differences.

RELATED APPLICATION

This application is related to copending U.S. application Ser. No.12/______, filed May 9, 2008 entitled “DETERMINING MECHANICAL FORCE ONANEURYSMS FROM A FLUID DYNAMIC MODEL DRIVEN BY VESSEL BLOOD FLOWINFORMATION.”

FIELD

This invention relates generally to angiography, and more particularlyto imaging of aneurysms.

BACKGROUND

Aneurysms are a fundamental cause of hemorrhagic stroke involving 20% ofall stroke cases. In aneurysm ruptures, a portion of the brain is filledblood that can cause tissue death or pressure in the head. Largehemorrhages can be fatal and are cause by clearly visible largeaneurysm. A particular case of interest is the debilitating “dementia”like conditions caused by micro hemorrhages that due to small aneurysmsruptures.

Aneurysms are infrequently encountered on a straight, non-branchingsegment of an intracranial artery. The aneurysms occurring on straight,non-branching segments are more often found to have sacs that pointlongitudinally along the wall of the artery in the direction of bloodflow and to project only minimally above the adventitial surface.Aneurysms having these characteristics are of a dissecting type, ratherthan of the congenital saccular type, and development of dissecting typeaneurysms is heralded more frequently by the onset of ischemicneurological deficits than by the subarachnoid hemorrhage associatedwith congenital saccular aneurysms.

Detecting small aneurysms is particularly difficult for CTA exams asthese are very minute and are often indistinguishable from thevasculature. Additionally, the presence of bone in the skull causesadded difficulty to visualize these structures.

BRIEF DESCRIPTION

In one aspect, the hazard of an aneurysm is measured by determiningchanges, and by determining a rate of change, of an aneurysm inlongitudinal exams using automated segmentation and quantificationtools.

In another aspect, accurate and reproducible volumetric measurements ofan aneurysm are provided by segmenting an image representation of theaneurysm from an image representation of an attached vasculaturefollowing detection and automating a workflow for the longitudinalfollow-up of the aneurysm by propagating a bookmark of the imagerepresentation of the aneurysm, segmenting the image representation ofthe aneurysm, quantifying the image representation of the aneurysm anddetermining a change in volume of the image representation of aneurysm.

In yet another aspect, a method to determine an aneurysm in a patientover time includes determining temporal differences between twosegmented aneurysms and visually presenting the temporal differences.

In yet a further aspect, a method of determining changes of ananatomical structure in a patient over time includes accessing an imagein a memory, segmenting a portion of the image containing an aneurysm,detecting the aneurysm in the segmented portion of the image, segmentingthe detected aneurysm in the image, quantifying the segmented detectedaneurysm in the image, visually presenting the segmented detectedaneurysm, storing a location of the segmented detected aneurysm imagedata to the memory, accessing a second image in the memory, propagatingthe location of the segmented detected aneurysm onto the second imageusing bookmarking or registration, segmenting a second detected aneurysmin the second image, quantifying the second segmented detected aneurysmin the second image, determining temporal differences between the firstsegmented detected aneurysm and the second segmented detected aneurism,and visually presenting the temporal differences between the firstsegmented detected aneurysm and the second segmented detected aneurism.

In still yet another aspect, a method to determine an aneurysm in apatient over time includes accessing at least two longitudinal exams,each longitudinal exam having an image, wherein the first longitudinalexam has a representation of a segmented aneurysm at a location in theimage, propagating the location of the segmented aneurysm of the firstlongitudinal exam onto an image of a subsequent longitudinal exam,detecting a second aneurysm in the vicinity of the propagated locationin the subsequent longitudinal exam, segmenting the second detectedaneurysm in the image of the second longitudinal exam, quantifying thesecond segmented detected aneurysm in the second image, determiningdifferences between the first segmented detected aneurysm and the secondsegmented detected aneurism, and visually presenting the differencesbetween the first segmented detected aneurysm and the second segmenteddetected aneurism.

In still yet a further aspect, a system includes a processor, a storagedevice coupled to the processor and operable to store a plurality ofimages, and software apparatus operable on the processor including aregistration module operable to propagate a location of an aneurysm froma first image of the plurality of images onto a second image of theplurality of images, a VCAR detector operable to receive each of theplurality of images, and a temporal comparator to identify changes inthe detected aneurism.

Systems, clients, servers, methods, and computer-readable media ofvarying scope are described herein. In addition to the aspects andadvantages described in this summary, further aspects and advantageswill become apparent by reference to the drawings and by reading thedetailed description that follows.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram of an overview of a system to determinechanges of an anatomical structure in a patient over time, according toan implementation;

FIG. 2 is a block diagram of an overview of a system to determinechanges of an anatomical structure in a patient over time, according toan implementation;

FIG. 3 is a flowchart of a method to determine changes of an anatomicalstructure in a patient over time, according to an implementation;

FIG. 4 is a flowchart of a method to determine changes of an anatomicalstructure in a patient over time, according to an implementation;

FIG. 5 is a flowchart of a method to determine changes of an anatomicalstructure in a patient over time, according to an implementation;

FIG. 6 is a flowchart of a method to determine changes of an anatomicalstructure in a patient over time, according to an implementation;

FIG. 7 is a flowchart of a method to determine changes of an anatomicalstructure in a patient over time, in which results are registered priorto processing by a VCAR so that the results are in the co-registeredspace, according to an implementation;

FIG. 8 is a flowchart of a method to determine changes of an anatomicalstructure in a patient over time, in which VCAR results are bookmarkedand registered prior to temporal comparison, according to animplementation;

FIG. 9 is a flowchart of a method of spherical shape determination,according to an implementation;

FIG. 10 is a flowchart of a method of longitudinal analysis, accordingto an implementation;

FIG. 11 is a flowchart of a method to image a patient, according to animplementation;

FIG. 12 is a flowchart of a method to determine vessel flow information,according to an implementation;

FIG. 13 is a flowchart of a method to determine vessel flow information,according to an implementation;

FIG. 14 is a simplified diagram of an overview of a modified systemconfigured to improve X-ray imaging operations; and

FIG. 15 illustrates an example of a general computer environment usefulin the context of the environment of FIG. 14, in accordance with anaspect of the disclosed subject matter.

DETAILED DESCRIPTION

In the following detailed description, reference is made to theaccompanying drawings that form a part hereof, and in which is shown byway of illustration specific implementations which may be practiced.These implementations are described in sufficient detail to enable thoseskilled in the art to practice the implementations, and it is to beunderstood that other implementations may be utilized and that logical,mechanical, electrical and other changes may be made without departingfrom the scope of the implementations. The following detaileddescription is, therefore, not to be taken in a limiting sense.

The detailed description is divided into four sections. In the firstsection, a system level overview is described. In the second section,implementations of methods are described. In the third section,particular implementations are described. Finally, in the fourthsection, a conclusion of the detailed description is provided.

System Level Overview

The systems in this section describe a workflow in which longitudinalexams are used to assess the seriousness of the aneurysm in eithercomputed tomography angiography (CTA) and/or magnetic resonance (MRA).

FIG. 1 is a block diagram of an overview of a system 100 to determinechanges of an anatomical structure in a patient over time, according toan implementation. System 100 determines and/or identifies mechanicalforce attributes of a vasculature represented in four-dimensional imagesof flow through the vasculature.

System 100 includes a vessel flow analyzer 104 that is operable todetermine vessel flow 106 of a vasculature from four-dimensional (4D)image data 102. In some embodiment, the 4D image data iscomputed-tomography data that is acquired using a cine dynamic imagingsystem or a GE shuttle mode (moving the patient to and fro in order toexpand image coverage) to acquire a time series of a contrast boluspassing through the tissue of interest. The expanded coverage to 8centimeter (CM) or more provides an image size in which a sufficientportion of the vasculature of the brain can be imaged in 4D. Thecontrast uptake in time in any of the arterial vessels allows for thecomputation of flow.

In some implementations of cerebral aneurysms, the vessel flow isdetermined using the volumetric dynamic data (4D) that is tracking thecontrast bolus through the neuron vasculature. The first part of themethod identifies two locations on a vessel (d1 and d2) that are adefined distance apart. The points on the vessel are identified at atime when the vessel is fully perfused with the contrast. Since the headis a rigid body and the motion in the vessels is primarily pulsatory dueto the heartbeat these landmarks can be easily backtracked in time to astage when there is no contrast in the vessel. The method of determiningthe flow in the vessel is to determine the times t1 and t2 when the twolandmark points on the vessel reach a steady state contrast enhancedvalue. The velocity of the fluid is determined by dividing the distancebetween the points by the time the fluid takes for the contrast totraverse that distance, as follows in equation 1:

$\begin{matrix}{{F = {V*A}}{where}{V = \frac{{d\; 2} - {d\; 1}}{{t\; 2} - {t\; 1}}}} & {{Equation}\mspace{14mu} 1}\end{matrix}$

In Equation 1, F represents the flow, V represents velocity and Arepresents the cross sectional area of the vessel.

System 100 also includes a mechanical force analyzer 108 that isoperable to determine mechanical force attributes 112 on a wall of ananeurism. In some implementations, the mechanical force analyzer 108determines the data representing mechanical force attributes 112 fromthe vessel flow data 106 using a fluid dynamics model 110.

In some implementations, the mechanical force attributes 112 vary inproportion to a velocity of a fluid in the vasculature in a directionperpendicular to a plane of shear in the vasculature.

In some implementations, the fluid dynamics model 110 includes aNavier-Stokes fluid dynamics model, as follows:

$\begin{matrix}{{\rho \frac{Dv}{Dt}} = {{\nabla \cdot} + {\rho \; f}}} & {{Equation}\mspace{14mu} 2}\end{matrix}$

In equation 2, ρ represents the fluid density, D/D_(t) represents asubstantive derivative (also called the material derivative), Vrepresents a velocity vector, f represents a body force vector,

represents a tensor that represents the surface forces applied on afluid particle (the comoving stress tensor). The substantive derivativeis a derivative taken with respect to a coordinate system moving withvelocity U.

Unless the fluid is made up of spinning degrees of freedom likevortices,

is a symmetric tensor. In general, (in three dimensions)

has the form:

$\begin{matrix}{= \begin{pmatrix}\sigma_{xx} & \tau_{xy} & \tau_{xz} \\\tau_{yx} & \sigma_{yy} & \tau_{yz} \\\tau_{zx} & \tau_{zy} & \sigma_{zz}\end{pmatrix}} & {{Equation}\mspace{14mu} 3}\end{matrix}$

In equation 3, σ represents normal stresses, and τ represents tangentialstresses (shear stresses). Equation 3 is a set of three equations, oneper dimension. By themselves, the three euqations of equation 3 areinsufficient to produce a solution. However, adding conservation of massand appropriate boundary conditions to the system of equations producesa solvable set of equations.

Or using the Newtonian fluid modeling whose shear stress is linearlyproportional to the velocity gradient in the direction perpendicular tothe plane of shear. This definition means regardless of the forcesacting on a fluid, the fluid continues to flow. For example, water is aNewtonian fluid, because the fluid continues to display fluid propertiesno matter how much the fluid is stirred or mixed. A slightly lessrigorous definition is that the drag of a small object being movedthrough the fluid is proportional to the force applied to the object.

In equations for a Newtonian fluid, the constant of proportionalitybetween the shear stress and the velocity gradient is known as theviscosity. A simple equation to describe Newtonian fluid behaviourfollows:

$\begin{matrix}{\tau = {\mu \frac{dv}{dx}}} & {{Equation}\mspace{14mu} 4}\end{matrix}$

In equation 4, τ represents shear stress exerted by the fluid (“drag”),μ represents the fluid viscosity, a constant of proportionality,D_(V)/D_(X) represents the velocity gradient perpendicular to thedirection of shear.

For a Newtonian fluid, the viscosity, by definition, depends only ontemperature and pressure, not on the forces acting upon it. If the fluidis incompressible and viscosity is constant across the fluid, theequation governing the shear stress (in Cartesian coordinates) follows:

$\begin{matrix}{\tau_{ij} = {\mu \left( {\frac{\partial v_{i}}{\partial x_{j}} + \frac{\partial v_{j}}{\partial x_{i}}} \right)}} & {{Equation}\mspace{14mu} 5}\end{matrix}$

In equation 5, τ_(ij) represent the shear stress on the i^(th) face of afluid element in the j^(th) direction v_(i) represents the velocity inthe i^(th) direction, x_(j) represents the j^(th) direction coordinate.If a fluid does not obey this relation, the fluid is termed anon-Newtonian fluid, of which there are several types.

FIG. 2 is a block diagram of an overview of a system 200 to determinechanges of an anatomical structure in a patient over time, according toan implementation. System 200 provide acquisition storage oflongitudinal images, segmentation of the images, and registration,comparison and reporting of results. FIG. 2 shows many differentcombinations of dataflow as indicated by bi-directional arrows.

System 200 includes a first image 202. The first image 202 is acquiredat a particular time, T₁.

A volume computer-assisted reading (VCAR) detector 204 is operable toreceive the first image 202, perform pre-segmentation and temporalanalysis on the image and transmit the first image 202 to a segmenter206.

More specifically, the segmentation performed by the VCAR detector 204includes pre-segmentation. Pre-segmentation ensures that unwanteddominating structures in the image data are not included in thesubsequent processing tasks. Examples of dominating structures mayinclude bone structures obscuring blood vessels and aneurysms, taggedmaterials obscuring the presence of polyps in colonography, cardiacchambers obscuring the coronary vessel tree visualization etc.

In many cases, one may remove the dominating unwanted structureobscuring salient features and proceed with subsequent processing steps.Alternatively, a mask corresponding to the dominating unwanted structuremay be extracted and used in the subsequent processing steps.

VCAR detector 204 is also capable of detecting an aneurysm: The firststep in detecting an aneurysm is shape detection, which is described ingreater detail in FIG. 9 below. Even though only spherical shapedetection is described in FIG. 9, the general method can be applicableto other shapes and texture detection.

A subsequent step performed by the VCAR detector 204 is extraction ofthe detected shape(s) which reduces the overlap of the disparateresponses by using a-priori anatomical information. For the illustrativeexample of the vascular aneurysm three-dimensional (3D) responses aredetermined using formulation using local curvature at implicitisosurfaces. The method termed curvature tensor determines the localcurvatures Kmin and Kmax in the null space of the gradient. Therespective curvatures can be determined using the following equation:

$\begin{matrix}{k_{i} = {\left( {{\min \mspace{11mu} \hat{v}},{\max \mspace{11mu} \hat{v}}} \right)\frac{{- {\hat{v}}^{T}}N^{T}{HN}\mspace{11mu} \hat{v}}{{\nabla\mspace{11mu} I}}}} & {{Equation}\mspace{14mu} 6}\end{matrix}$

In equation 6, k represents a curvature, v represent at a vector in theN null space of the gradient of image data I with H being a Hessian ofimage I. The solution to equation 2 are the eigen values of thefollowing equation:

$\begin{matrix}\frac{{- N^{T}}{HN}}{{\nabla\; I}} & {{Equation}\mspace{14mu} 7}\end{matrix}$

In equation 7, responses of the curvature tensor (Kmin and Kmax) aresegregated into spherical and cylindrical responses based on thresholdson Kmin, Kmax and the ratio of Kmin/Kmax derived from the size andaspect ratio of the sphericalness and cylindricalness that is ofinterest, in one exemplary formulation the aspect ratio of 2:1 and aminimum spherical diameter of 1 mm with a maximum of 20 mm is used. Itshould be noted that a different combination would result in a differentshape response characteristic that would be applicable for a differentanatomical object.

The disparate responses so established do have overlapping regions thatcan be termed as false responses. The differing acquisition parametersand recon processes and noise characteristics of recon processes are amajor source of these false responses. A method of removing the falseresponses is adjusting the threshold values to compensate for thediffering acquisitions. The adjusting includes creating a mapping of thethresholds to all possible acquisition. Mapping the thresholds includesutilizing anatomical information in the form of the scale of theresponses on large vessels (cylindrical responses) and the intentionalbiasing of the response towards spherical vs. cylindrical to come upwith the use of morphological closing of the cylindrical response volumeto cull any spherical responses that are in the intersection of the“closed” cylindrical responses and the spherical response. An exemplaryresponse is shown in FIG. 2.

In general, the segmenter 206 is operable to segment an aneurysm in theimage 202. The segmenter 206 provides automated or manual means forisolating regions of interest. In many cases of practical interest, theentire image can be the region of interest. After an object is detected,a region of interest (ROI) needs to be extracted to calculate featuresin the data. This step is application dependent. Several techniques orcombinations of techniques can be used for this purpose including butnot limited to iterative adaptive thresholding, k-means segmentation,edge detection, edge linking, curve fitting, curve smoothing, 2D/3Dmorphological filtering, region growing, fuzzy clustering, image/volumemeasurements, heuristics, knowledge-based rules, decision trees, neuralnetworks. The segmentation of the region of interest can be performedeither manually and/or automatically. The manual segmentation involvesdisplaying the data and a user delineating the region using a mouse orany other suitable interface (e.g. touch screen, eye-tracking, voicecommands). An automated segmentation algorithm can use prior knowledgesuch as the shape and size of a mass to automatically delineate the areaof interest. A semi-automated method which is the combination of theabove two methods may also be used.

A quantifier 208 is operable to receive the segmented image and toquantify the segmented aneurysm in the image 202.

A visualizer 210 is operable to visualize the segmented aneurysm. Thevisualizer 210 module provides the display and quantificationcapabilities for the user to visualize and or quantify the results oftemporal comparison. In practice, one can use all the available temporalimage-pairs for the analysis. The comparison results can be displayed inmany ways, including textual reporting of quantitative comparisons,simultaneous overlaid display with current or previous images using alogical operator based on some pre-specified criterion, color look-uptables can be used to quantitatively display the comparison, ortwo-dimensional or three-dimensional cine-loops can be used to displaythe progression of change for image to image. The resultant image canalso be coupled with an automated or manual pattern recognitiontechnique to perform further qualitative and/or quantitative analysis ofthe comparative results. The results of this further analysis can bedisplay alone or in conjunction with the acquired images using any ofthe methods described above.

In some implementations, visualizer 210 performs volume rendering whichprovide visualization of three-dimensional data. Volume rendering is atechnique of visualizing sampled functions of three spatial dimensionsby computing 2-D projections of a semitransparent volume.

Currently, a major application area of volume rendering is medicalimaging, where volume data is available from X-ray Computed Tomography(CT) scanners. CT scanners produce three-dimensional stacks of parallelplane images, each of which consist of an array of X-ray absorptioncoefficients. Typically X-ray CT images will have a resolution of512*512*12 bits and there will be up to 500 slices in a stack. In thetwo-dimensional domain, these slices can be viewed one at a time. Theadvantage of CT images over conventional X-ray images is that CT imagesonly contain information from that one plane. A conventional X-rayimage, on the other hand, contains information from all the planes, andthe result is an accumulation of shadows that are a function of thedensity of the tissue, bone, organs, etc., anything that absorbs theX-rays. The availability of the stacks of parallel data produced by CTscanners prompted the development of techniques for viewing the data asa three-dimensional field rather than as individual planes. This gavethe immediate advantage that the information can be viewed from anyviewpoint.

Rendering techniques include rendering voxels in binary partitionedspace, marching cubes, and ray casting. In regards to rendering voxelsin binary partitioned space, choices are made for the entire voxel,which can produce a “blocky” image. In regards to marching cubes reducesthe blocky aspect, but causes some other problems. In regards to raycasting, the best use of the three-dimensional data is provided withoutan attempt to impose any geometric structure on the three-dimensionaldata. Ray casting solves one of the most important limitations ofsurface extraction techniques, namely the way in which ray castingdisplays a projection of a thin shell in the acquisition space. Surfaceextraction techniques fail to take into account that, particularly inmedical imaging, data may originate from fluid and other materials,which may be partially transparent and should be modeled as such, ofwhich ray casting does not suffer.

Other types of visualization include virtual reality, virtualexamination, immersive visualization, multiple image fusion,synchronized viewing in multiple view ports (4D/4D, 4D/3D, 4D/2D, 4D/1D,4D/0D, 3D/3D, 3D/2D, 3D/1D, 3D/0D, 2D/2D, 2D/1D, 2D/0D), overlayvisualization (text, symbol, feature, texture, color, shape, indicator,combination) etc.

System 200 includes a second image 212. The second image 212 wasacquired at a particular time, T₁. A volume computer-assisted reading(VCAR) detector 214 is operable to receive the second image 212 andtransmit the second image 212 to a segmenter 216. The VCAR detector 214is operable to perform the same functions as the VCAR detector 204. Thesegmenter 216 is operable to segment an aneurysm in the image 212. Aquantifier 218 is aorta at the circuit and operable to receive thesegmented image and to quantify the segmented aneurysm in the image 212.A visualizer 211 is operable to visualize the segmented aneurysm. Insome implementations, the segmenter 206, the quantifier 208, and thevisualizer 210 are the same component as the segmenter 216, thequantifier 218 and a visualizer 220, respectively.

A registration module 222 module provides methods of registration. Ifthe regions of interest for temporal change analysis are small, rigidbody registration transformations including translation, rotation,magnification, and shearing may be sufficient to register a pair ofimages from S1 and S2. However, if the regions of interest are largeincluding almost the entire image, warped, elastic transformationsusually have to be applied. One way to implement the warped registrationis to use a multi-scale, multi-region, pyramidal approach. In thisapproach, a different cost function highlighting changes may beoptimized at every scale. An image is resampled at a given scale, andthen the image is divided into multiple regions. Separate shift vectorsare calculated at different regions. Shift vectors are interpolated toproduce a smooth shift transformation, which is applied to warp theimage. The image is resampled and the warped registration process isrepeated at the next higher scale until the pre-determined final scaleis reached. Other methods of registration can be substituted here aswell. Some of the well-known techniques involve registering based on themutual information histograms. These methods are robust enough toregister anatomic and functional images. For the case of single modalityanatomic registration, we prefer the method described above where as forthe single modality functional registration, we prefer to use mutualinformation histograms.

The registration module 222 is also operable to propagate a location ofan aneurysm from the first image 202 onto a second image 212.

The VCAR detector 204 is operable to process registration informationassociated with each of the plurality of images.

A temporal comparator 224 is operable to identify changes in thedetected aneurism. In particular, the temporal comparator 224 isoperable to identify changes in the segmented portions of the two images202 and 212. The temporal comparator 224 is also operable to identifychanges in the quantifications of the segmented aneurysms. The temporalcomparator 124 is also operable to identify changes in thevisualizations of the segmented aneurysms. It should be noted that eventhough temporal VCAR with respect an aneurysm VCAR is described, thegeneric methodology can be expanded to temporal processing of any VCARprocess.

Some implementations of the temporal comparator 224 includes a varietyof different methods for comparing temporally separated anatomicalparts, these include; comparison after quantification has been done,e.g. comparing the change in volume of a lesion or aneurysm, comparisonfrom a visual point of view e.g. comparing the shape and size of aanatomical part to assess any change in a qualitative manner, comparisonof bookmarked locations (and acceptance/rejection of the bookmarkedlocations) e.g. validating the registration module by verifying that thebookmarks have propagated to the right anatomical location in the twoexams. In addition computational comparisons can also be performed toimprove the conspicuity of change e.g. for mono-modality temporalprocessing, the prior art methods obtain difference image D1_(a)=(S1*S2)/(S2*S2+Φ), where the scalar constant Φ>0. In thedegenerative case of Φ=0 which is not included here, the above equationbecomes a straightforward division, S1/S2.

While the system 200 is not limited to any particular images 202 and212, VCAR detectors 204 and 214, segmenters 206 and 216, quantifiers 208and 218, and visualizers 210 and 220, registration module 222,comparator 224, segmentation change detector 226, quantification changedetector 228, and visualization change picture 230, for sake of claritya simplified images 202 and 212, VCAR detectors 204 and 214, segmenters206 and 216, quantifiers 208 and 218, and visualizers 210 and 220,registration module 222, comparator 224, segmentation change detector226, quantification change detector 228, and visualization changepicture 230 are described.

In applications of system 200, the data collected at t_(n−1) and t_(n)can be processed in different ways as shown in FIG. 2. First methodinvolves performing independent VCAR operations on each of the data setsand performing the comparison analysis on the final outputs visually.Second method involves merging the results prior to the segmentationstep. Third method involves registering data prior to featureidentification step. Fourth method proposed involves a combination ofthe above methods. Additionally, the proposed method also includes astep to register images to the same coordinate system. Optionally, imagecomparison results following registration of two data sets can also bethe additional input to the segmentation step. Thus, the proposed methodleverages temporal differences and feature commonalities to arrive at amore synergistic analysis of temporal data.

Additionally, other methods can be derived as further described below.

When a new image is acquired, such as image 202, the image can beprocessed by VCAR 204. In parallel, the newly acquired image can beregistered with a previously stored image and the registered previousimage can be processed by VCAR 214. The images processed by VCAR 204 andVCAR 214 can be temporally compared and visualized to provide animproved temporal comparison. See FIG. 7 for further details.

In another example, images from two different time instances are VCARprocessed and the results are book marked. The book marked results areregistered prior to temporal comparison. In this case, image-to-imageregistration is not needed. See FIG. 8 for further details.

System 200 provides three-dimensional (3D) quantification of brainaneurysms for the purpose of surgical planning and the correspondingevaluation study. System 200 uses implicit deformable models combinedwith non-parametric statistical information to quantify aneurysmmorphology and to obtain clinically relevant parameters. System 200supports computerized surgical planning of coiling procedures byallowing more accurate and truly 3D quantification of brain aneurysms.

System level overviews of the operation of implementations are describedabove in this section of the detailed description. Some implementationsof system 100 and system 200 operate in a multi-processing,multi-threaded operating environment on a computer.

Method Implementations

In the previous section, a system level overview of the operation of animplementation is described. In this section, the particular methods ofsuch an implementation are described by reference to a series offlowcharts. Describing the methods by reference to a flowchart enablesone skilled in the art to develop such programs, firmware, or hardware,including such instructions to carry out the methods on suitablecomputers, executing the instructions from computer-readable media.Similarly, the methods performed by the server computer programs,firmware, or hardware are also composed of computer-executableinstructions. Methods 300-1300 are performed by a program executing on,or performed by firmware or hardware that is a part of, a computer.

FIG. 3 is a flowchart of a method 300 to determine changes of ananatomical structure in a patient over time, according to animplementation.

Method 300 includes accessing an image in a memory, at block 302.

Some implementations of method 300 include segmenting a portion of theimage containing an aneurysm, at block 304. Some implementations of thesegmenting at block 304 include eliminating unwanted data in the image.Other implementations of the segmenting at block 304 include generatinga mask that excludes unwanted data in the image.

Some implementations of method 300 include detecting the aneurysm in thesegmented portion of the image, at block 306. Some implementations ofdetecting the aneurysm at block 306 include detecting an aneurysm in theimage while excluding unwanted data in the image. Other implementationsof detecting the aneurysm at block 306 include detecting an aneurysm inthe image in reference to a mask that excludes unwanted data in theimage.

Some implementations of method 300 include segmenting the detectedaneurysm in the image, at block 308.

Some implementations of method 300 include quantifying the segmenteddetected aneurysm in the image, at block 310. In some implementations ofmethod 300, the detecting of the aneurysm at block 306, the segmentingof the detected aneurysm at block 308 and quantifying the segmenteddetected aneurysm at block 310 are all performed simultaneously.

Some implementations of method 300 include visually presenting thesegmented detected aneurysm, at block 312.

Some implementations of method 300 include storing a location of thesegmented detected aneurysm image data to the memory, at block 314.

Some implementations of method 300 include accessing a second image inthe memory, at block 316.

FIG. 4 is a flowchart of a method 400 to determine changes of ananatomical structure in a patient over time, according to animplementation.

Method 400 includes accessing a second image in a memory, at block 402.The first longitudinal exam has a representation of a segmented aneurysmat a location in the image and propagating a location of the segmenteddetected aneurysm onto the second image using bookmarking orregistration, at block 404.

Some implementations of method 400 also include segmenting a seconddetected aneurysm in the second image, at block 406.

Some implementations of method 400 also include quantifying the secondsegmented detected aneurysm in the second image, at block 408. Someimplementations of method 400 also include segmenting the seconddetected aneurysm in the image of the second longitudinal exam, at block410. Some implementations of method 400 also include determiningdifferences between the first segmented aneurysm and the secondsegmented aneurism, at block 412.

Some implementations of method 400 also include visually presenting thedifferences between the first segmented detected aneurysm and the secondsegmented detected aneurism, at block 414.

Some implementations of method 400 also include bookmarking the resultsof the detection and registering the bookmarked results beforedetermining the differences.

FIG. 5 is a flowchart of a method 500 to determine changes of ananatomical structure in a patient over time, according to animplementation.

Some implementations of method 500 include accessing data, at block 502,and some implementations of method 500 include pre-segmenting the data,at block 504.

Some implementations of method 500 also include masking thepre-segmented data, at block 506.

Some implementations of method 500 also include processing of the maskeddata by a volume computer-assisted reading (VCAR) detector (e.g. VCAR204 in FIG. 2), at block 508.

FIG. 6 is a flowchart of a method 600 to determine changes of ananatomical structure in a patient over time, according to animplementation.

Some implementations of method 600 include accessing data, at block 502.Some implementations of method 600 include pre-segmenting the data, atblock 504.

Some implementations of method 600 also include processing of thepre-segmented data by a volume computer-assisted reading (VCAR) detector(e.g. VCAR 204 in FIG. 2, at block 602.

FIG. 7 is a flowchart of a method 700 to determine changes of ananatomical structure in a patient over time, in which results areregistered prior to processing by a VCAR so that the results are in theco-registered space, according to an implementation.

Some implementations of method 700 include acquisition of an image, atblock 702.

In some implementations of 700, the acquired image is stored in anonvolatile storage, such as an acquisition module, and thereafteraccessed, at block 704. The acquisition storage module stores acquiredor synthesized images. For temporal change analysis, means are providedto retrieve the data from storage corresponding to an earlier timepoint. To simplify notation in the subsequent discussion we describeonly two images to be compared, even though the general approach can beextended for any number of images in the acquisition and temporalsequence. Let S1 and S2 be the two images to be registered and compared.

The accessed image is registered at block 706 and processed by a volumecomputer-assisted reading (VCAR) detector (e.g. VCAR 204 in FIG. 2).

Subsequently a second image is acquired, at block 702, and the secondimage is processed by a volume computer-assisted reading (VCAR) detector(e.g. VCAR 204 in FIG. 2), at block 710. Thereafter, a temporalcomparison is performed on the first registered image in the secondimage, at block 712. The temporal comparison at block 712 identifieschanges of an anatomical structure in a patient over time.

Thereafter, the changes in the anatomical structure in the patient overtime are visualized, at block 714.

FIG. 8 is a flowchart of a method 800 to determine changes of ananatomical structure in a patient over time, in which VCAR results arebookmarked and registered prior to temporal comparison, according to animplementation.

Method 800 includes acquisition of a first image, at block 702. Thefirst image is processed by a volume computer-assisted reading (VCAR)detector (e.g. VCAR 204 in FIG. 2), at block 708.

A second image in storage is accessed, at block 704. The second image isprocessed by a VCAR detector, at block 710.

Subsequently, the two VCAR-processed images are registered, and block804. Thereafter, a temporal comparison is performed on the firstregistered image in the second image, at block 712. The temporalcomparison at block 712 identifies changes of an anatomical structure ina patient over time. In method 700, image-to-image registration is notneeded.

Thereafter, in some implementations, the changes in the anatomicalstructure in the patient over time are visualized, at block 714.

FIG. 9 is a flowchart of a method 900 of spherical shape determination,according to an implementation.

Method 900 includes acquiring at block 902, at least two images.Examples of the images include image 102 and image 112.

Method 900 also includes processing the plurality of images with athree-dimensional (3D) filter, at block 904. Thereafter, shapeprimitives are generated from the processed data, at block 906.

In method 900, an original spherical response is generated from theshape primitives, at block 908. Thereafter a complete spherical response(should that be “clean response”) is generated from the originalspherical response using culling, at block 910. Action 910 is performedby culling responses that overlap with the complete cylindricalresponse.

In method 900, an original cylindrical response is generated from theshape primitives, at block 912. Thereafter a complete cylindricalresponse (should that be “clean response”) is generated from theoriginal cylindrical response using morphological closing, at block 914.Action 914 is based on anatomy and acquisition parameters.

Subsequently method 900 includes displaying the responses generated atblock 912 and block 916. In some implementations method 900 alsoincludes pruning the spherical responses using shape analysis, at block918.

FIG. 10 is a flowchart of a method 1000 of longitudinal analysis,according to an implementation.

Method 1000 includes accessing at least two longitudinal exams, at block1002. In action 1002, two exams that were acquired at significantlydifferent times for a meaningful change assessment of the anomaly, i.e.aneurysm, are accessed. A key element of this action is the loading ofthe stored location of the aneurysm from the earlier exam. This actioninvolves loading of data at time instance t₁ and at least at timeinstance t₂, as well as loading the location (with other attributes) ofall aneurysms at time instance t₁.

Method 1000 also includes propagating at least one location of ananeurysm from a first exam of the longitudinal exams to a second exam ofthe longitudinal exams, at block 1004. The propagation of locations 1004involves the automatic propagation of the aneurysm location from theexam at time instance t₁ to time instance t₂, which is accomplished by aregistration of the two datasets so that the locations are propagated inan accurate manner. Some implementations provide an option for a user tointeract with the propagated locations and perform adjustments ifneeded.

Method 1000 also includes segmenting and quantifying the aneurysm at thelocation, at block 1006. Segmentation and quantification of aneurysms inboth exams at the validated location determine the volume (and otherproperties, like texture, shape, etc.) of the aneurysms at both timeinstances t₁ and t₂. Method 100 is not affected by changes occurring innon-aneurysm areas and the results are targeted toward only aneurysmlike shapes.

Thereafter method 1000 also includes determining and displaying at leastone change in quantitation of the aneurysm, at block 1008. There areseveral quantifications that can be done including but not limited todetermining number of clusters, area, path, shape, size, boundary,volume, location, statistics, skeleton etc. The change in size and/orappearance of the aneurysm from time t₁ to time t₂ is displayed to theuser so that the user can make an assessment for the next course ofaction.

FIG. 11 is a flowchart of a method 1100 to image a patient, according toan implementation.

Method 1100 includes accessing four-dimensional image data that tracksand/or describes contrast flow through a vasculature, at block 1102.Method 1100 also includes determining vessel flow information of thevasculature from the four-dimensional image data, at block 1104. Oneimplementation of determining vessel flow information at block 1104 isshown in FIG. 12 below.

Method 1100 also includes determining mechanical force attributes (e.g.stress and strain) on a wall of an aneurism, at block 1106. Themechanical force attributes are determined from the vessel flowinformation using a fluid dynamics model in which the mechanical forceattributes varies in proportion (e.g. linearly) to a velocity of fluidin a proximity to a location in the vasculature in a direction that isperpendicular to a plane of shear in the vasculature. Method 1100 alsoincludes visually presenting the determined mechanical force attributesin three-dimensional rendition of the volumetric data using visual cuesto indicate a degree of mechanical force attributes, at block 1108.

Some implementations of method 1100 also include generating a report ofthe determined mechanical force attributes, at block 1110 and thenstoring the report, at block 1112.

Some implementations of method 1100 also include determining and/orproviding treatment based on the determined mechanical force attributes,at block 1114.

FIG. 12 is a flowchart of a method 1200 to determine vessel flowinformation, according to an implementation. Method 1200 is one exampleof determining vessel flow information at block 1104 in FIG. 11.

Method 1200 includes detecting a velocity of contrast uptake in avessel, at block 1202. One example of detecting the velocity of contrastuptake in the vessel at block 1202 is shown in FIG. 13. Method 1200 alsoincludes detecting a cross-sectional area of the vessel, at block 1204.

FIG. 13 is a flowchart of a method 1300 to determine vessel flowinformation, according to an implementation. Method 1300 is one exampleof detecting velocity of contrast uptake in a vessel, at block 1202 inFIG. 12.

Method 1300 includes identifying two points on a vessel being a distanceapart from each other, at block 1302. Method 1300 also includesdetermining a time for each of the two points of which flow in thevessel reaches a steady state contrast enhanced value, at block 1304.

Method 1300 also includes determining the velocity by dividing thedistance by the difference of the two times, at block 1306.

In some implementations, methods 300-1300 are implemented as a computerdata signal embodied in a carrier wave, that represents a sequence ofinstructions which, when executed by a processor, such as a processor,cause the processor to perform the respective method. In otherimplementations, methods 300-1300 are implemented as acomputer-accessible medium having executable instructions capable ofdirecting a processor to perform the respective method. In varyingimplementations, the medium is a magnetic medium, an electronic medium,or an optical medium.

The following description provides an overview of computer hardware anda suitable computing environment in conjunction with which someimplementations can be implemented. Implementations are described interms of a computer executing computer-executable instructions. However,some implementations can be implemented entirely in computer hardware inwhich a computer-executable instructions are implemented in read-onlymemory. Some implementations can also be implemented in client/servercomputing environments where remote devices that perform tasks arelinked through a communications network. Program modules can be locatedin both local and remote memory storage devices in a distributedcomputing environment.

The aspects of class hierarchy 500 and use cases 600, 700, 800, 900,1000, 1100, 1200 and 1300 shown in FIG. 5-13 can be embodied as computerhardware circuitry or as a computer-readable program, or a combinationof both. In other aspects, the systems and method described herein areimplemented in an application service provider (ASP) system.

More specifically, in the computer-readable program aspect, the programscan be structured in an object-orientation using an object-orientedlanguage such as Java, Smalltalk or C++, and the programs can bestructured in a procedural-orientation using a procedural language suchas COBOL or C. The software components communicate in any of a number ofmeans that are well-known to those skilled in the art, such asapplication program interfaces (API) or interprocess communicationtechniques such as remote procedure call (RPC), common object requestbroker architecture (CORBA), Component Object Model (COM), DistributedComponent Object Model (DCOM), Distributed System Object Model (DSOM)and Remote Method Invocation (RMI). The components execute on as few asone computer as in computer 1500 in FIG. 15, or on at least as manycomputers as there are components.

FIG. 14 is a simplified diagram of an overview of a modified system 1400configured to improve X-ray imaging operations. The system 1400optionally includes a gantry 1402 or other support for an illuminationsource 1404, such as an X-ray illumination source, capable of providingillumination 1406, such as X-rays or other non-destructive internalimaging illumination, and can optionally include a test subject support1408 that is transmissive with respect to the illumination 1406 and thatis positioned above a scintillator 1409 and detector 1410 that is alsoopposed to the illumination source 1404. Alternatively, a directconversion detector 1410 can be employed without need for ascintillator.

In one aspect, components of the system 1400 and a test subject 1412 aremaintained in a defined geometric relationship to one another by thegantry 1402. A distance between the illumination source 1404 and thedetector 1410 can be varied, depending on the type of examinationsought, and the angle of the illumination 1406 respective to the testsubject 1412 can be adjusted with respect to the body to be imagedresponsive to the nature of imaging desired.

In one aspect, the test subject support 1408 is configured to supportand/or cause controlled motion of the test subject 1412, such as aliving human or animal patient, or other test subject 1412 suitable fornon-destructive imaging, above the scintillator 1409/detector 1410 sothat illumination 1407 is incident thereon after passing through thetest subject 1412. In turn, information from the detector array 1410describes internal aspects of the test subject 1412.

The scintillator 1409 can be a conventional CsI scintillator 1409,optically coupled to an array of photodiodes (not illustrated), such asa two-dimensional array of photodiodes and suitable control transistorsformed using semiconductor material such as amorphous silicon, or anyother form of detector 1410 suitable for use with the type or types ofillumination 1406 being employed, such as X-rays. The detector elementsare typically tesselated in a mosaic. The scintillator 1409 convertsincident photons comprising electromagnetic radiation, such as X-rays,from high-energy, high-frequency photons 1407, into lower-energy,lower-frequency photons corresponding to spectral sensitivity of thedetector elements, in a fashion somewhat analogous to fluorescence, asis commonly known in the context of many visible-light sources in usetoday. Alternatively, the detector 1410 can be formed as a flat-panelarray including amorphous Silicon (α-Si) active elements, together witheither a scintillator layer 1409, or a direct converter material such asCadmium Zinc Telluride (CdZnTe), Mercuric Iodide (Hgl₂), Lead Iodide(Pbl₂), or amorphous Selenium (α-Se).

In some modes of operation, such as CT, the gantry 1402 and test subjectsupport or table 1408 cooperatively engage to move the test subject 1412longitudinally within an opening 1414, that is, along an axis 1416extending into and out of the plane of FIG. 14. In some modes ofoperation, the gantry 1402 rotates the X-ray source 1404 and detector1410 about the axis 1416, while the support 1408 moves longitudinally,to provide a helical series of scans of the test subject 1412, where apitch of the helices is defined as a ratio of a longitudinal distancetraveled by the table 1408 during a complete revolution of the gantry1402, compared to a length of the detector 1410 along the axis 1416 oflinear motion.

The system 1400 also optionally includes a control module or controller1420. The controller 1420 can include a motor control module 1422configured to move the test subject support 1408 and thus the testsubject 1412 relative to the X-ray source 1404 and/or detector 1410, andcan also control motors in the gantry 1402 or to position the X-rayillumination source 1404 relative to the test subject 1412 and/or thedetector 1410.

The controller 1420 includes a detector controller 1424 configured tocontrol elements within the detector 1410 and to facilitate datatransfer therefrom. The controller 1420 also includes a drive parametercontroller 1428 configured to control electrical drive parametersdelivered to the X-ray source 1404. One or more computers 1430 provideconnections to the controller 1420 via a bus 1432 configured forreceiving data descriptive of operating conditions and configurationsand for supplying appropriate control signals. Buses 1434, 1437 and 1439act to transfer data and control signals, for example with respect to amodule 1435, configured as an image processing engine, viainterconnections such as 1437, 1439 that are configured for exchange ofsignals and data to and/or from the computer 1430 as well as otherelements of the system 1400 and/or external computation orcommunications resources (not illustrated in FIG. 14).

The system 1400 also includes a bus 1436, a bus 1438 and an operatorconsole 1440. The operator console 1440 is coupled to the system 1400through the bus 1434. The operator console 1440 includes one or moredisplays 1442 and a user input interface 1444. The user input interface1444 can include a touchscreen, keyboard, a mouse or other tactile inputdevice, capability for voice commands and/or other input devices. Theone or more displays 1442 provide video, symbolic and/or audioinformation relative to operation of system 1400, user-selectableoptions and images descriptive of the test subject 1412, and can displaya graphical user interface for facilitating user selection among variousmodes of operation and other system settings.

The image processing engine 1435 facilitates automation of accuratemeasurement and assessment. The image processing engine 1435 is capableof forming multiple, coordinated images for display, for example via themonitor 1442, to provide the types of depictions described below. Theimage processing engine 1435 can comprise a separate and distinctmodule, which can include application-specific integrated circuitry, orcan comprise one or more processors coupled with suitablecomputer-readable program modules, or can comprise a portion of thecomputer 1430 or other computation device.

The system 1400 also includes memory devices 1450, coupled via the bus1436 to the computer 1430 through suitable interfaces. Datasetsrepresenting three-dimensional data and image or two-dimensional datatypically conform to the digital imaging and communications in medicine(DICOM) standard, which is widely adopted for handling, storing,printing, and transmitting information in medical imaging. The DICOMstandard includes a file format definition and a network communicationsprotocol. The communication protocol is an application protocol thatuses TCP/IP to communicate between systems. DICOM files can be stored inmemory devices 1450 and retrieved therefrom, and can be exchangedbetween two entities that are capable of receiving image and patientdata in DICOM format.

The memory devices 1450 include mass data storage capabilities 1454 andone or more removable data storage device ports 1456. The one or moreremovable data storage device ports 1456 are adapted to detachablycouple to portable data memories 1458, which can include optical,magnetic and/or semiconductor memories and can have read and/or writecapabilities, and which can be volatile or non-volatile devices or caninclude a combination of the preceding capabilities.

The system 1400 further includes a data acquisition and conditioningmodule 1460 that has data inputs coupled to the detector 1410 and thatis coupled by the bus 1438 to the one or more computers 1430. The dataacquisition and conditioning module 1460 includes analog to digitalconversion circuitry for capturing analog data from the detector 1410and then converting those data from the detector 1410 into digital form,to be supplied to the one or more computers 1430 for ultimate displayvia at least one of the displays 1442 and for potential storage in themass storage device 1454 and/or data exchange with remote facilities(not shown in FIG. 14). The acquired image data can be conditioned ineither the data acquisition and conditioning module 1460 or the one ormore computers 1430 or both.

The system 1400 also includes a power supply 1470, coupled viainterconnections represented as a power supply bus 1472, shown in dashedoutline, to other system elements, and a power supply controller 1474.In some aspects, the system 1400 is configured to be a mobile systemequipped with a portable power supply 1470, such as a battery. In otherwords, the system 1400 can comprise a wheeled unit and can beelectromotively powered in self-contained fashion, lending physicalagility to the ensemble of attributes offered by the system 1400.

Volumetric data collected via exposure of the test subject 1412 tosuitable illumination 1406 can be processed via many different types oftools, each intended to enhance some portion of information contentdescribed by the data. One result can be inconsistency betweenanalytical results from different types of signal processing tools, orbetween measurement results corresponding to different measurement timesand/or measurement phases.

FIG. 15 illustrates an example of a general computer environment 1500useful in the context of the environment of FIG. 14, in accordance withan aspect of the disclosed subject matter. The general computerenvironment 1500 includes a computation resource 1502 capable ofimplementing the processes described herein. It will be appreciated thatother devices can alternatively used that include more components, orfewer components, than those illustrated in FIG. 15.

The illustrated operating environment 1500 is only one example of asuitable operating environment, and the example described with referenceto FIG. 15 is not intended to suggest any limitation as to the scope ofuse or functionality of the aspects of this disclosure. Other well-knowncomputing systems, environments, and/or configurations can be suitablefor implementation and/or application of the subject matter disclosedherein.

The computation resource 1502 includes one or more processors orprocessing units 1504, a system memory 1506, and a bus 1508 that couplesvarious system components including the system memory 1506 toprocessor(s) 1504 and other elements in the environment 1500. The bus1508 represents one or more of any of several types of bus structures,including a memory bus or memory controller, a peripheral bus, anaccelerated graphics port and a processor or local bus using any of avariety of bus architectures, and can be compatible with SCSI (smallcomputer system interconnect), or other conventional bus architecturesand protocols.

The system memory 1506 includes nonvolatile read-only memory (ROM) 1602and random access memory (RAM) 1512, which can or can not includevolatile memory elements. A basic input/output system (BIOS) 1514,containing the elementary routines that help to transfer informationbetween elements within computation resource 1502 and with externalitems, typically invoked into operating memory during start-up, isstored in ROM 1510.

The computation resource 1502 further can include a non-volatileread/write memory 1516, represented in FIG. 15 as a hard disk drive,coupled to bus 1508 via a data media interface 1517 (e.g., a SCSI, ATA,or other type of interface); a magnetic disk drive (not shown) forreading from, and/or writing to, a removable magnetic disk 1520 and anoptical disk drive (not shown) for reading from, and/or writing to, aremovable optical disk 1526 such as a CD, DVD, or other optical media.

The non-volatile read/write memory 1516 and associated computer-readablemedia provide nonvolatile storage of computer-readable instructions,data structures, program modules and other data for the computationresource 1502. Although the exemplary environment 1500 is describedherein as employing a non-volatile read/write memory 1516, a removablemagnetic disk 1520 and a removable optical disk 1526, it will beappreciated by those skilled in the art that other types ofcomputer-readable media which can store data that is accessible by acomputer, such as magnetic cassettes, FLASH memory cards, random accessmemories (RAMs), read only memories (ROM), and the like, can also beused in the exemplary operating environment.

A number of program modules can be stored via the non-volatileread/write memory 1516, magnetic disk 1520, optical disk 1526, ROM 1510,or RAM 1512, including an operating system 1530, one or more applicationprograms 1532, other program modules 1534 and program data 1536.Examples of computer operating systems conventionally employed for sometypes of three-dimensional and/or two-dimensional medical image datainclude the NUCLEUS® operating system, the LINUX® operating system, andothers, for example, providing capability for supporting applicationprograms 1532 using, for example, code modules written in the C++®computer programming language.

A user can enter commands and information into computation resource 1502through input devices such as input media 1538 (e.g., keyboard/keypad,tactile input or pointing device, mouse, foot-operated switchingapparatus, joystick, touchscreen or touchpad, microphone, antenna etc.).Such input devices 1538 are coupled to the processing unit 1504 througha conventional input/output interface 1542 that is, in turn, coupled tothe system bus. A monitor 1550 or other type of display device is alsocoupled to the system bus 1508 via an interface, such as a video adapter1552.

The computation resource 1502 can include capability for operating in anetworked environment (as illustrated in FIG. 14, for example) usinglogical connections to one or more remote computers, such as a remotecomputer 1560. The remote computer 1560 can be a personal computer, aserver, a router, a network PC, a peer device or other common networknode, and typically includes many or all of the elements described aboverelative to the computation resource 1502. In a networked environment,program modules depicted relative to the computation resource 1502, orportions thereof, can be stored in a remote memory storage device suchas can be associated with the remote computer 1560. By way of example,remote application programs 1562 reside on a memory device of the remotecomputer 1560. The logical connections represented in FIG. 15 caninclude interface capabilities, e.g., such as interface capabilities1452 (FIG. 14) a storage area network (SAN, not illustrated in FIG. 15),local area network (LAN) 1572 and/or a wide area network (WAN) 1574, butcan also include other networks.

In certain aspects, the computation resource 1502 executes an InternetWeb browser program (which can optionally be integrated into theoperating system 1530), such as the “Internet Explore®” Web browsermanufactured and distributed by the Microsoft Corporation of Redmond,Wash.

When used in a LAN-coupled environment, the computation resource 1502communicates with or through the local area network 1572 via a networkinterface or adapter 1576. When used in a WAN-coupled environment, thecomputation resource 1502 typically includes interfaces, such as a modem1578, or other apparatus, for establishing communications with orthrough the WAN 1574, such as the Internet. The modem 1578, which can beinternal or external, is coupled to the system bus 1508 via a serialport interface.

In a networked environment, program modules depicted relative to thecomputation resource 1502, or portions thereof, can be stored in remotememory apparatus. It will be appreciated that the network connectionsshown are exemplary, and other means of establishing a communicationslink between various computer systems and elements can be used.

A user of a computer can operate in a networked environment 1400 usinglogical connections to one or more remote computers, such as a remotecomputer 1560, which can be a personal computer, a server, a router, anetwork PC, a peer device or other common network node. Typically, aremote computer 1560 includes many or all of the elements describedabove relative to the computer 1500 of FIG. 15.

The computation resource 1502 typically includes at least some form ofcomputer-readable media. Computer-readable media can be any availablemedia that can be accessed by the computation resource 1502. By way ofexample, and not limitation, computer-readable media can comprisecomputer storage media and communication media.

Computer storage media include volatile and nonvolatile, removable andnon-removable media, implemented in any method or technology for storageof information, such as computer-readable instructions, data structures,program modules or other data. The term “computer storage media”includes, but is not limited to, RAM, ROM, EEPROM, FLASH memory or othermemory technology, CD, DVD, or other optical storage, magneticcassettes, magnetic tape, magnetic disk storage or other magneticstorage devices, or any other media which can be used to storecomputer-intelligible information and which can be accessed by thecomputation resource 1502.

Communication media typically embodies computer-readable instructions,data structures, program modules or other data, represented via, anddeterminable from, a modulated data signal, such as a carrier wave orother transport mechanism, and includes any information delivery media.The term “modulated data signal” means a signal that has one or more ofits characteristics set or changed in such a manner as to encodeinformation in the signal in a fashion amenable to computerinterpretation.

By way of example, and not limitation, communication media include wiredmedia, such as wired network or direct-wired connections, and wirelessmedia, such as acoustic, RF, infrared and other wireless media. Thescope of the term computer-readable media includes combinations of anyof the above.

The computer 1502 can function as one or more of the control segments ofmodule 1420 (FIG. 14), the computer 1430, the operator console 1440and/or the data acquisition and conditioning module 1460, for example,via implementation of the process 200 FIG. 2, respectively, as one ormore computer program modules.

Conclusion

An aneurysm imaging system is described. A technical effect is temporalcomparison of aneurysm in longitudinal exams. Although specificimplementations have been illustrated and described herein, it will beappreciated by those of ordinary skill in the art that any arrangementwhich is calculated to achieve the same purpose may be substituted forthe specific implementations shown. This application is intended tocover any adaptations or variations. For example, although described inprocedural terms, one of ordinary skill in the art will appreciate thatimplementations can be made in an object-oriented design environment orany other design environment that provides the required relationships.

In particular, one of skill in the art will readily appreciate that thenames of the methods and apparatus are not intended to limitimplementations. Furthermore, additional methods and apparatus can beadded to the components, functions can be rearranged among thecomponents, and new components to correspond to future enhancements andphysical devices used in implementations can be introduced withoutdeparting from the scope of implementations. One of skill in the artwill readily recognize that implementations are applicable to futurecommunication devices, different file systems, and new data types.

The terminology used in this application is meant to include allimaging, database and communication environments and alternatetechnologies which provide the same functionality as described herein.

1. A method of determining changes of an anatomical structure in apatient over time, the method comprising: accessing an image in amemory; segmenting a portion of the image containing an aneurysm;detecting the aneurysm in the segmented portion of the image; segmentingthe detected aneurysm in the image; quantifying the segmented detectedaneurysm in the image; visually presenting the segmented detectedaneurysm; storing a location of the segmented detected aneurysm imagedata to the memory; accessing a second image in the memory; propagatingthe location of the segmented detected aneurysm onto the second imageusing bookmarking or registration; segmenting a second detected aneurysmin the second image; quantifying the second segmented detected aneurysmin the second image; determining temporal differences between the firstsegmented detected aneurysm and the second segmented detected aneurism;and visually presenting the temporal differences between the firstsegmented detected aneurysm and the second segmented detected aneurism.2. The method of claim 1, wherein the segmenting further comprises:eliminating unwanted data in the image.
 3. The method of claim 1,wherein the segmenting further comprises: generating a mask thatexcludes unwanted data in the image.
 4. The method of claim 1, whereinthe detecting further comprises: detecting an aneurysm in the imagewhile excluding unwanted data in the image.
 5. The method of claim 1,wherein the detecting further comprises: detecting an aneurysm in theimage in reference to a mask that excludes unwanted data in the image.6. The method of claim 1, wherein the detecting, the segmenting and thequantifying further comprise: being performed at one point in time.
 7. Acomputer-accessible medium having executable instructions to determinean aneurysm in a patient over time, the executable instructions capableof directing a processor to perform: determining temporal differencesbetween two segmented aneurysms; and visually presenting the temporaldifferences.
 8. The computer-accessible medium of claim 7, wherein theexecutable instructions further comprise executable instructions capableof directing the processor to perform: accessing an image; segmenting aportion of the image containing an aneurysm; detecting an aneurysm inthe image; segmenting the detected aneurysm in the image; quantifyingthe segmented detected aneurysm in the image; visually presenting thesegmented detected aneurism; and storing a location of the segmenteddetected aneurysm image data
 9. The computer-accessible medium of claim8, wherein the executable instructions capable of directing theprocessor to perform segmenting further comprise executable instructionscapable of directing the processor to perform: eliminating unwanted datain the image.
 10. The computer-accessible medium of claim 8, wherein theexecutable instructions capable of directing the processor to performsegmenting further comprise executable instructions capable of directingthe processor to perform: generating a mask that excludes unwanted datain the image.
 11. The computer-accessible medium of claim 8, wherein theexecutable instructions capable of directing the processor to performdetecting further comprise executable instructions capable of directingthe processor to perform: detecting an aneurysm in the image whileexcluding unwanted data in the image.
 12. The computer-accessible mediumof claim 8, wherein the executable instructions capable of directing theprocessor to perform detecting further comprise executable instructionscapable of directing the processor to perform: detecting an aneurysm inthe image in reference to a mask that excludes unwanted data in theimage.
 13. The computer-accessible medium of claim 8, wherein theexecutable instructions capable of directing the processor to performdetecting, the segmenting and the quantifying further compriseexecutable instructions capable of directing the processor to perform:the detecting, the segmenting and the quantifying at one point.
 14. Acomputer-accessible medium having executable instructions to determinean aneurysm in a patient over time, the executable instructions capableof directing a processor to perform: accessing at least two longitudinalexams, each longitudinal exam having an image, wherein the firstlongitudinal exam has a representation of a segmented aneurysm at alocation in the image; propagating the location of the segmentedaneurysm of the first longitudinal exam onto an image of a subsequentlongitudinal exam; detecting a second aneurysm in the vicinity of thepropagated location in the subsequent longitudinal exam; segmenting thesecond detected aneurysm in the image of the second longitudinal exam;quantifying the second segmented detected aneurysm in the second image;determining differences between the first segmented detected aneurysmand the second segmented detected aneurism; and visually presenting thedifferences between the first segmented detected aneurysm and the secondsegmented detected aneurism.
 15. The computer-accessible medium of claim14, wherein the executable instructions further comprise executableinstructions capable of directing the processor to perform: bookmarkingthe results of the detection; and registering the bookmarked resultsbefore determining the differences.
 16. A system comprising: aprocessor; a storage device coupled to the processor and operable tostore a plurality of images; software apparatus operable on theprocessor comprising: a registration module operable to propagate alocation of an aneurysm from a first image of the plurality of imagesonto a second image of the plurality of images; a VCAR detector operableto receive each of the plurality of images; and a temporal comparator toidentify changes in the detected aneurism.
 17. The system of claim 16,further comprising: a segmenter of a second detected aneurysm in thesecond image; a quantifier of the second segmented detected in thesecond image.
 18. The system of claim 16, wherein the VCAR is furtheroperable to: process the plurality of images with a 3D filter; generateshape primitives from the processed data; and display a response fromthe shape primitives.
 19. The system of claim 16, wherein the VCAR isfurther operable to: generate an original spherical response; generatean original cylindrical response; generate a clean spherical responseusing culling; and generate a complete cylindrical response usingmorphological closing.
 20. The system of claim 16, wherein the VCAR isfurther operable to: process registration information associated witheach of the plurality of images.
 21. The system of claim 16, wherein theregistration module is further operable to: register results from theVCAR associated with each of the plurality of images.